{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from math import ceil\n",
    "from sklearn.metrics import accuracy_score, log_loss\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "from utils.input_pipeline import get_image_folders\n",
    "from utils.diagnostic import top_k_accuracy, count_params,\\\n",
    "    entropy, model_calibration, predict\n",
    "\n",
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from get_densenet import get_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, val_folder = get_image_folders()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model, _, _ = get_model()\n",
    "\n",
    "# load pretrained quantized model\n",
    "model.load_state_dict(torch.load('model_ternary_quantization.pytorch_state'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7151176"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of params in the model\n",
    "count_params(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Show some quantized kernel tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# all quantized kernels\n",
    "all_kernels = [\n",
    "    (n, p.data) for n, p in model.named_parameters() \n",
    "    if ('denseblock' in n or 'transition' in n) and 'conv' in n\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vDtlfks6RtEzSbZL2Lc1nXo5/r6R5o1slMzMzMzMzs97UiW/CXx8Re0fE7Hx+KnBtROwO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0mOqeOfleYUpK8nuSnJK8bW+95a+4NJrk/yfXXa+5L82tg6Lk/yM+PrLqV8qpTyu6WU3aWUbwH/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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f530ceeecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show distribution of weights in kernels that are in the beginning of the network \n",
    "_, axes = plt.subplots(nrows=6, ncols=4, figsize=(14, 10))\n",
    "axes = axes.flatten()\n",
    "for i, (name, kernel) in enumerate(all_kernels[:24]):\n",
    "    axes[i].hist(kernel.cpu().numpy().reshape(-1));\n",
    "    axes[i].set_title(name[9:-7]);\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wG/ClWkU+j3NcYboDByBeMzMzMzMzs44wGM3XpwAX5e6LgPcUyi+OZC6wmaQtgAOA6yJi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\nckmSJEmSBsRGuSRJkiRJA/L/AWVFiaLxXpAtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f53638a7e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# kernels in the end of the network \n",
    "_, axes = plt.subplots(nrows=6, ncols=4, figsize=(14, 10))\n",
    "axes = axes.flatten()\n",
    "for i, (name, kernel) in enumerate(all_kernels[-24:]):\n",
    "    axes[i].hist(kernel.cpu().numpy().reshape(-1));\n",
    "    axes[i].set_title(name[9:-7]);\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sparcity distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparcity = []\n",
    "for n, p in all_kernels:\n",
    "    sparcity.append(p.eq(0.0).float().mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDHdJashwl6SGDHdJauj/AInCUHw4T8p0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f53638a59e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sparcity);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88301851569103595"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(sparcity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scaling factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_scaling_factor = []\n",
    "lower_scaling_factor = []\n",
    "for n, p in all_kernels:\n",
    "    upper_scaling_factor.append(p.max())\n",
    "    lower_scaling_factor.append(p.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJakR/wv3ao/+a7FtjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f53638a5710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(upper_scaling_factor);\n",
    "plt.hist(lower_scaling_factor);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Error analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get all predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_iterator_no_shuffle = DataLoader(\n",
    "    val_folder, batch_size=64, shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 157/157 [00:17<00:00,  8.99it/s]\n"
     ]
    }
   ],
   "source": [
    "val_predictions, val_true_targets = predict(model, val_iterator_no_shuffle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### logloss and accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3499565506832443"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_loss(val_true_targets, val_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.65910000000000002"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(val_true_targets, val_predictions.argmax(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.76649999999999996, 0.81630000000000003, 0.84870000000000001, 0.86880000000000002, 0.92049999999999998]\n"
     ]
    }
   ],
   "source": [
    "print(top_k_accuracy(val_true_targets, val_predictions, k=(2, 3, 4, 5, 10)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### number of misclassified images (there are overall 10000 images in the val dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3409"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hits = val_predictions.argmax(1) == val_true_targets\n",
    "(~hits).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### entropy of predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YWdKdyXriz0o6Kdn0KNAjqeWLdd7TUH+TJM2X9ATwePLa1ZJWJjVfX2sf303e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\npuesxXXMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f530c234e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    entropy(val_predictions[hits]), bins=30, \n",
    "    normed=True, alpha=0.7, label='correct prediction'\n",
    ");\n",
    "plt.hist(\n",
    "    entropy(val_predictions[~hits]), bins=30, \n",
    "    normed=True, alpha=0.5, label='misclassification'\n",
    ");\n",
    "plt.legend();\n",
    "plt.xlabel('entropy of predictions');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### confidence of predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TtpUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f530c234b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    val_predictions[hits].max(1), bins=30, \n",
    "    normed=True, alpha=0.7, label='correct prediction'\n",
    ");\n",
    "plt.hist(\n",
    "    val_predictions[~hits].max(1), bins=30, \n",
    "    normed=True, alpha=0.5, label='misclassification'\n",
    ");\n",
    "plt.legend();\n",
    "plt.xlabel('confidence of predictions');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### difference between biggest and second biggest probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f530c2349e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_correct = np.sort(val_predictions[hits], 1)\n",
    "sorted_incorrect = np.sort(val_predictions[~hits], 1)\n",
    "\n",
    "plt.hist(\n",
    "    sorted_correct[:, -1] - sorted_correct[:, -2], bins=30, \n",
    "    normed=True, alpha=0.7, label='correct prediction'\n",
    ");\n",
    "plt.hist(\n",
    "    sorted_incorrect[:, -1] - sorted_incorrect[:, -2], bins=30, \n",
    "    normed=True, alpha=0.5, label='misclassification'\n",
    ");\n",
    "plt.legend();\n",
    "plt.xlabel('difference');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### probabilistic calibration of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J4oaVK1fmYIRKpc3WL/tixpj1WFogJ4wx7wFP2i8spfIPYwyLgk7y2Geb+P1w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\nLk0USiml0qWJQimlVLo0USillErX/wPElGsNZn5sywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f530c1257b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_calibration(val_true_targets, val_predictions, n_bins=10)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
